At some point, ChatGPT stopped feeling like a tool and started feeling like a reflex.

Need a parser? Ask ChatGPT.
Strange exception in a background worker? Ask ChatGPT.
Forgot how a lock behaves under contention? Ask ChatGPT.
Need to rename a method? Apparently, that also required artificial intelligence.

Nothing looked wrong on the surface. Tasks were moving. Pull requests were getting merged. The code usually worked. Sometimes it even looked cleaner than what I would have written from scratch. But there was a small problem. A few days later, I often could not explain why a certain solution was built that way. I remembered the task, the final code, and maybe the prompt. The reasoning in between was missing.

That bothered me more than I expected.

So I set a simple rule: for six months, no ChatGPT, no Copilot Chat, no AI-generated code pasted into production. Documentation, source code, issue trackers, books, debuggers, profilers, and search engines were allowed. AI assistants were not.

The experiment started as a way to test my own dependence. It ended up changing how I design APIs, debug systems, read unfamiliar code, and even write comments.

Our office has roughly one hundred employees, but for a long time we had no proper help desk process. Requests arrived through email, corporate chat, phone calls, and random conversations in the hallway. It always felt as if most of the day was spent dealing with servers, networks, backups, and serious incidents.

To check whether that impression was accurate, I logged every support request for one month. I measured active work time, recorded the cause of recurring problems, tracked waiting periods, and counted how often a new request interrupted something already in progress.

Over 22 working days, I recorded 496 contacts. After merging duplicate reports related to the same issue, those contacts became 421 separate support episodes.

The most surprising result was not the total number of requests. It was the damage caused by short five‑minute tasks constantly breaking longer technical work into small, nearly useless fragments.

This article explains how I collected the data, why average resolution time is often misleading, how I automated Windows workstation diagnostics with PowerShell, and how I processed the resulting event log using Python and PostgreSQL.

Полгода назад я понял, что мы проигрываем не конкурентам и не техдолгу. Мы проигрываем времени суток. В этой статье — мой практический разбор того, как я замерял хронотипы команды, собирал телеметрию активности, анализировал коммиты и встречи, строил простенькую модель продуктивности и в итоге полностью перестроил рабочий график удалённой команды. Будет немного биологии, немного математики, немного кода и много личных факапов.